Automatic Neural Architecture Search Based on an Estimation of Distribution Algorithm for Binary Classification of Image Databases

Erick Franco-Gaona, Maria Susana Avila-Garcia and Ivan Cruz-Aceves ()
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Erick Franco-Gaona: Departamento de Estudios Multidisciplinarios, División de Ingenierías, Campus Irapuato-Salamanca Universidad de Guanajuato, Av. Universidad S/N, Yuriria 38944, Guanajuato, Mexico
Maria Susana Avila-Garcia: Departamento de Estudios Multidisciplinarios, División de Ingenierías, Campus Irapuato-Salamanca Universidad de Guanajuato, Av. Universidad S/N, Yuriria 38944, Guanajuato, Mexico
Ivan Cruz-Aceves: SECIHTI-Centro de investigación en Matemáticas (CIMAT), Valenciana 36023, Guanajuato, Mexico

Mathematics, 2025, vol. 13, issue 4, 1-19

Abstract: Convolutional neural networks (CNNs) are widely used for image classification; however, setting the appropriate hyperparameters before training is subjective and time consuming, and the search space is not properly explored. This paper presents a novel method for the automatic neural architecture search based on an estimation of distribution algorithm (EDA) for binary classification problems. The hyperparameters were coded in binary form due to the nature of the metaheuristics used in the automatic search stage of CNN architectures which was performed using the Boltzmann Univariate Marginal Distribution algorithm (BUMDA) chosen by statistical comparison between four metaheuristics to explore the search space, whose computational complexity is O ( 2 29 ). Moreover, the proposed method is compared with multiple state-of-the-art methods on five databases, testing its efficiency in terms of accuracy and F1-score. In the experimental results, the proposed method achieved an F1-score of 97.2%, 98.73%, 97.23%, 98.36%, and 98.7% in its best evaluation, better results than the literature. Finally, the computational time of the proposed method for the test set was ≈0.6 s, 1 s, 0.7 s, 0.5 s, and 0.1 s, respectively.

Keywords: Boltzmann Univariate Marginal Distribution; classification; convolutional neural network; estimation of distribution algorithms; neural architecture search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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